Specifies the maximum number of offset days that can be applied to an item’s safety stock calculation.

A common misconception is that limiting the offset days alone will restrict safety stock calculations. However, the risk percentage also plays a significant role in determining safety stock. These two values are calculated independently.

When using these parameters, assess whether the business tends to over- or under-forecast, and whether excess stock or stockouts would cause greater harm.

Changing these global settings may conceal item-level risk variations that would otherwise highlight where investigation and correction are needed.

➜ Refer to this article for a detailed explanation of Supply Risk and Offset Explained and this article for a detailed explanation of how the The Recommended Order Quantity (ROQ) Calculation Explained.

Let’s look at Maximum (%), Minimum (%), Maximum Offset, Minimum Offset, Lead Time Cut-off (deviation), and Lead Time Cut-off (%) collectively.

Risk offset indicates the additional days that need to be added to or subtracted from the calculated safety stock days.

To determine how risky a supplier is considered, review supplier performance: Do they deliver on time and in full, or do they frequently deliver late or short?

Imagine an item with a planning lead time of 14 days. This means an order for this item from this supplier will be recommended 14 days before the item is due to be sold. If, in reality, the average delivery duration has been 50 days, the item will arrive 36 days late.

A risk offset value of 36 days will therefore be calculated, adding 36 days of safety stock to the original value.

This is the simple explanation — there are many nuances not covered in this article.

The risk percentage indicates the degree of variability in the data — how scattered the lead time results are.

Example:
If a supplier delivered four orders in 51, 48, 49, and 52 days, the average lead time is 50 days with little variation.

If deliveries were 16, 17, 90, and 77 days, the average is still 50 days, but the variation is large. This makes it difficult for the app to predict reliability and results in a higher risk percentage.

A common misconception is that a large risk percentage always results in large risk offset days. This is not necessarily true, as these two values are calculated mostly independently.

Safety Stock is calculated using five key factors:

These factors interact to determine the required safety stock, except for risk offset days, which are applied after the calculation.

It is therefore possible to have:

Limiting Minimum Risk (%), Maximum Risk (%), Minimum Offset (days), and Maximum Offset (days) becomes crucial when the goal is to limit safety stock due to cash flow constraints or capital tied up in inventory.

However, enforcing cutoffs for these parameters could result in additional stock-outs and prevent the business from achieving its target fill rates.

Take note that changing global settings can obscure item-level nuances that might have been better addressed by investigating the source of high risk.

⚠️ Remember: You can also set cutoffs for safety stock directly in the Configuration settings.

Lead time cut-off aims to exclude data which may skew our calculation of the average lead time. Risk percentage calculates the degree of variability. Now imagine having a historic lead time data point so large that it ends up increasing your risk percentage and thus your safety stock. For this reason it could be useful setting the cut-off threshold in terms of a percentage or standard deviation.

Suppose the average historical lead time for a supplier is 50 days.

If the lead time cut-off percentage is set to 60%, the system will exclude any delivery that falls more than 60% above or below this average.

Let’s calculate:

Therefore, any measured lead times below 20 days or above 80 days will be ignored when calculating supply risk.

This ensures that a few extreme deliveries do not make the supplier appear far less reliable than they actually are.

Suppose the same supplier has an average historical lead time of 50 days, but the delivery times vary widely, for example, 16, 17, 90, and 77 days.

In this case, you could use the standard deviation to define a dynamic threshold.

If the cut-off standard deviation is set to 3, then any lead time that falls outside three standard deviations from the mean will be excluded.

This approach adapts to the amount of data available, a tighter spread of lead times will naturally have smaller deviations, while more variable suppliers will have larger deviations.

⚠️ Note: The app uses the more generous of the two cut-offs when excluding outliers. This means the measured lead time of an order must lie outside the threshold of both cut-offs. This can make it challenging to exclude extreme readings when there are very few readings, due to very high standard deviations.

⚠️ Note: These cut-off settings only apply to the supply risk calculation. The lead time measurement calculation uses an internal fixed cut-off of 3 standard deviations.

Question: How is standard deviation calculated?
​Answer: Standard deviation is a mathematical measure of variation. It can be reviewed in any mathematical reference or textbook for detailed calculation steps.

Question: Why use lead time cut-off (%) instead of lead time cut-off (deviation)?
​Answer: It’s largely a matter of preference. Statistically inclined users may prefer standard deviation, but using both ensures fringe data points are accounted for safely.